Principal Component-Based Spectral Standardization for Optical Spectrometers.
Saved in:
| Title: | Principal Component-Based Spectral Standardization for Optical Spectrometers. |
|---|---|
| Authors: | Yang, Qiguang1 (AUTHOR), Liu, Xu2 (AUTHOR) xu.liu-1@nasa.gov, Wu, Wan2,3 (AUTHOR), Bhatt, Rajendra1,2 (AUTHOR), Shea, Yolanda2 (AUTHOR), Xiong, Xiaozhen2,3 (AUTHOR), Zhao, Ming1 (AUTHOR), Smith, Paul3 (AUTHOR), Kopp, Greg3 (AUTHOR), Pilewskie, Peter3 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 8, p1209. 23p. |
| Subjects: | Spectrometers, Calibration, Principal components analysis, Radiance, Hyperspectral imaging systems, Wavelength measurement |
| Abstract: | Highlights: What are the main findings? A novel, highly accurate principal component-based spectral standardization (PCSS) method is developed for standardizing measured spectra to a standard wavelength grid. The PCSS method was validated with both simulated spectra and real measured data. What are the implications of the main findings? The high accuracy and low uncertainties enable reliable spectral standardization and intercalibration across different satellite sensors. The method is computationally efficient, making the approach practical for operational implementation. A Principal Component-Based Spectral Standardization (PCSS) method was developed to standardize hyperspectral radiance spectra onto a fixed wavelength grid. This enables the direct comparison of radiance or reflectance spectra across different spatial pixels of an imaging spectrometer or between different instruments. The method was validated using simulated Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) spectra. The PCSS approach demonstrated high accuracy: the average root-mean-square uncertainty across all CPF channels remained below 0.07%, with maximum individual-channel uncertainties under 1%. Compared to methods based on spectral interpolation, PCSS produced significantly lower biases with tighter error distributions, particularly in spectrally rich regions. Measured Hyper Spectral Imager for Climate Science (HySICS) balloon data provided further validation. PCSS successfully estimated wavelength shifts that closely matched measured data, even when utilizing approximated Jacobians, demonstrating the method's robustness. Because it relies on a pre-computed lookup table for model parameters, PCSS bypasses the need for intensive radiative transfer calculations, making it highly computationally efficient. Beyond CPF, this method can easily be adapted for other hyperspectral sensors by substituting their respective wavelength grids and instrument line shape functions, offering a powerful tool to improve cross-calibration between different satellite sensors. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? A novel, highly accurate principal component-based spectral standardization (PCSS) method is developed for standardizing measured spectra to a standard wavelength grid. The PCSS method was validated with both simulated spectra and real measured data. What are the implications of the main findings? The high accuracy and low uncertainties enable reliable spectral standardization and intercalibration across different satellite sensors. The method is computationally efficient, making the approach practical for operational implementation. A Principal Component-Based Spectral Standardization (PCSS) method was developed to standardize hyperspectral radiance spectra onto a fixed wavelength grid. This enables the direct comparison of radiance or reflectance spectra across different spatial pixels of an imaging spectrometer or between different instruments. The method was validated using simulated Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) spectra. The PCSS approach demonstrated high accuracy: the average root-mean-square uncertainty across all CPF channels remained below 0.07%, with maximum individual-channel uncertainties under 1%. Compared to methods based on spectral interpolation, PCSS produced significantly lower biases with tighter error distributions, particularly in spectrally rich regions. Measured Hyper Spectral Imager for Climate Science (HySICS) balloon data provided further validation. PCSS successfully estimated wavelength shifts that closely matched measured data, even when utilizing approximated Jacobians, demonstrating the method's robustness. Because it relies on a pre-computed lookup table for model parameters, PCSS bypasses the need for intensive radiative transfer calculations, making it highly computationally efficient. Beyond CPF, this method can easily be adapted for other hyperspectral sensors by substituting their respective wavelength grids and instrument line shape functions, offering a powerful tool to improve cross-calibration between different satellite sensors. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18081209 |
